**7. Conclusions**

Vineyards that are certified organic and biodynamic, however, are not necessarily the same ones that are early- or significant-adopters of latest BD and AI technology that can accelerate and support the wider transformation from conventional to sustainable vitiviniculture practices. As discussed, this is because of a disconnect that exists between the path to adoption of sustainable practices and the path to adoption of BD and AI technology. This could be addressed by providing a way to structure and integrate an expert knowledge and insights from all stakeholders into an ES embedded within an overarching analytical framework. The majority of research challenges identified in this review, which span a wide range of aspects of viniviticulture, also point to the need for including expert knowledge to provide context and rules to design AI algorithms and their automated learning, while helping to structure data, obtain high-quality data for training AI models, and validate the use and adoption of new BD types and sources. Aligning the existing AFEO ontology that links vitiviniculture objects and experimental activities to an analytical BD and AI modeling, could accelerate the advancement of sustainable vitiviniculture. This would also provide the ES methodology with an ability to learn from experience which most systems cannot do currently. ML and DL models and algorithms need to be trained and informed by an ES that integrates imprecise and

*Artificial Intelligence and Big Data Analytics in Vineyards: A Review DOI: http://dx.doi.org/10.5772/intechopen.99862*

vague information as well as qualitative data and decision rule-based logic that is used in stakeholder decision making. This will require linking the scientific and expert knowledge on climate and weather risks pertaining to drivers and interactions, the BD value chain, to address the identified research challenges outlined here. Future work will aim to synthesize knowledge and insights from the wide array of applications of ES, to design a representative ES for the proposed BD value chain.
